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Raiders roster 2026: Remaining free agent
Are there some in-house free agents that could still return
Silicon Valley-Themed 'Breaking The Code' Boarded By Two Sales Reps.
The Silicon Valley-themed 'Breaking the Code,' about pioneering Indian entrepreneur Kanwal Rekhi, has been boarded by two international sales agents.
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Luxury realtors are spending thousands on Botox, skincare, and clothes for work
Luxury real-estate agents are spending thousands on skincare, Botox, and designer clothes to project the aspirational lifestyle their clients want.
Acti puts AI agents directly into your smartphone keyboard
Startup Acti is betting the smartphone keyboard is the next home for AI assistants. Its new keyboard for iOS and Android works across apps and lets users create custom AI-powered shortcuts using natural language.
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AI agents are the latest “next big thing” for AI, but OpenClaw has been working at the idea for a...
Cursor now has a mobile app for guiding your coding agent on the go
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AI scientist company Edison Scientific tapped by team behind Metsera to create new biotechs
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The Golden State Warriors reportedly have their eye on several players ahead of NBA free agency.
AI coding agents gain trust as developers skip reviews
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This Week’s Small Business Tech News: Apple Significantly Raises Their Prices
AI is reshaping small business IT, customer service and marketing. Learn why Microsoft agents, Apple price hikes and AI influencers matter to your business.
How Domo went from a $2.8 billion darling to fighting for its survival
Domo has been rocked by executive departures, new competition from AI agents, and the drunken-driving arrest of its founder and CEO, Josh James.
Notion killing Skiff-influenced email app since most users use AI agents instead
Notion is "going all in on using agents to run your inbox."
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
2026 NBA draft: Tracking the top undrafted free agent signings
Rookie Wire is tracking the top undrafted free agents following the conclusion of the 2026 NBA draft on Wednesday.
Assort Health Raises $120 Million Series C to Scale Largest Deployment of AI Agents for the Patient Journey
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Early Hamilton County business focused on agriculture, survival
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Second Annual Cenevo Survey of Life Science Professionals Reveals Future of AI in Modern Labs
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Blue state’s anti-ICE pledge collapses as GOP warns of new sanctuary ‘confederacy’
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Newsom says he's on Trump's 'hit list' as Justice Department interviews governor's friends
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Fantasy Football Roundtable: These free agents are still out there — here's where we'd like them to land
Apple Could Eventually Bring An OpenClaw-Like Competitor To Multiple Platforms, Offering Personalized AI Agents At Flexible Pricing
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Google AI Mode starts rolling out Search agents that keep track of information for you
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FBI searches office of Ohio group that supports voter registration efforts
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Genetically modified worms can now produce and deliver drugs inside a living body, scientists say
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Wix Brings the Wix Headless Business Backend to Stripe Projects
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Amazon partnership establishes Cornell AI security initiative
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Jedify raises $24M to help companies arm AI agents with context on their business
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Jedify raises $24M to give enterprise AI agents the business context they lack
Jedify raises $24M to give enterprise AI agents the business context they lack - SiliconANGLE
Can AI Agents Replicate Science? Argonne's Rick Stevens Puts Them to the Test
Have we reached a point where AI agents can reliably function as scientific collaborators? Can they go one step further and work as autonomous scientists? Stevens is an Associate Laboratory […]
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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Apple approves Poke as the first AI agent on its Messages for Business platform
Poke, the startup that lets people use AI agents through simple text messages, has become the first AI agent approved for Apple’s Messages for Business platform.
Apple approves Poke as the first AI agent on its Messages for Business platform
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Radial Entertainment Greenlights 20 New Episodes of 'FBI Files' True Crime Series
Content distributor Radial Entertainment June 4 announced that production is underway on 20 new episodes of the true crime series “The FBI Files.” Bright North Studios, backed by Jeff Zucker-led RedBird IMI, will produce the new episodes. Produced in cooperation with the Federal Bureau of Investigation, “The FBI Files” follows agents as they investigate the … Continue reading "Radial Entertainment Greenlights 20 New Episodes of ‘FBI Files’ True Crime Series"
A political split in Oregon's Columbia River Gorge
Visit Hood River, as so many people do, and you’ll see on the front windows of many downtown businesses a sign saying: “We are immigrants,” and sometimes next to them signs saying, “No trespassing — no federal agents — agents lacking judicial warrants will be turned away.” Many took care to advertise an April 23 […]
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Meta is trying to sell AI agents to businesses in latest effort to diversify away from ads
The Meta Business Agent will be included in the company's Meta One paid subscription offerings.
Factorial Raises $150 Million to Expand AI Agents in Business Software
Venture-capital firm General Catalyst led the funding round—that also attracted interest from Atomico and Four Rivers—as AI reshapes the business-management software market.
The 7 AI Agent Guardrails Every Business Needs Before Things Go Wrong
AI agents promise to automate work, make decisions and transform business operations, but giving machines more autonomy also creates new risks.
Microsoft Build 2026: the 7 biggest announcements
A Surface dev kit, an OS for AI agents, and more.
Inside Microsoft’s Project Solara: A new platform for devices that run AI agents instead of apps
A team inside Microsoft has been quietly building Project Solara, a platform for devices that run AI agents instead of apps, based on Android instead of Windows. The first two concept devices, a desktop hub and a wearable badge, are headed to pilots with companies including AccuWeather, Best Buy, CVS Health, Levi's, and Target.
Microsoft launches MXC, an OS-level sandbox for AI agents, with OpenAI and Nvidia already on board
Microsoft launches MXC, an OS-level sandbox for AI agents in Windows, giving enterprises secure runtime controls, identity, and policy enforcement.
Nvidia and Microsoft Researchers Say AI Agents Don't Care About Safety or Reliability
The researchers compared AI to the near-sighted cartoon character Mr. Magoo, who can’t see he’s stumbling through dangerous situations.
Nvidia chases $200B CPU market with AI agent PCs from Microsoft, Dell, and HP
If Nvidia has cracked a way to bring AI agents easily, safely and usefully to the masses, it could — and should — be big.
ASUS Unveils Revolutionary ProArt PCs Powered by NVIDIA RTX Spark at Computex 2026
ASUS unveils the new ProArt P16, P14 laptops and ProArt Mini PC powered by NVIDIA RTX Spark at Computex 2026. Discover 1 petaflop AI performance, 128GB unified memory, Lumina Pro OLED displays, all-day battery life, and local AI agents launching fall 2026.
Nvidia launches ‘superchip’ putting AI power into laptops and PCs
Firm says its RTX Spark PC chip for Microsoft Windows will let AI agents replace the mouse and keyboard
DuckDuckGo's 'No AI' Search Traffic Climbs as Users Reject Google's AI Overhaul
Privacy-focused search engine DuckDuckGo has seen a surge in demand for its "No AI" search option in the wake of Google's May 19th I/O announcements. Google debuted a new "intelligent" search box reimagined with AI. It features AI suggestions as an upgrade to autocomplete, support for follow-up questions, expanded Personal Intelligence for connecting Gmail and Google Photos, and Search agents.
Hochul signs measures to limit ICE cooperation, restrict mask use by ICE agents
The package represents one of the primary actions taken by Hochul and the state Legislature to push back against the Trump administration.
Trump's DOJ sues 4 Democratic-run states over denying undercover license plates for federal agents
The Department of Justice alleges in separate lawsuits announced Thursday that Maine, Massachusetts, Oregon, and Washington state are imposing unconstitutional restrictions that it says impede law enforcement and threaten agents’ safety.
Trump's DOJ sues Maine over denying undercover license plates for federal agents
President Donald Trump's administration is suing four states over their refusal to issue undercover license plates to federal agents, the latest front in the wider struggle between the White House and Democratic-led states over the Republican president's immigration crackdown.
Vikes Views: Veteran Free Agents Still Available
As the Minnesota Vikings ramp up the meat of the offseason program with OTAs and minicamp just days away, the roster is largely set but not necessarily finished. Often, veteran free agents remain available who can still make a meaningful impact. The Vikings are going to depend more on younger players at DL and LB […]
Marc Benioff said Salesforce is still hiring more in one department
Marc Benioff discusses the role in Salesforce that AI agents are yet to be able to replace, and it's not that of an engineer.
How AWS SMGS uses an AI-powered conversational assistant to transform business management with Amazon Bedrock AgentCore
In this post, we share how we built NarrateAI using Amazon Bedrock AgentCore to deliver business intelligence at scale for the AWS SMGS (Sales, Marketing and Global Services) organization. You will learn about: the two-layer architecture that separates batch processing from real-time interaction, the specialized AI agents that power intelligent routing and validation, key engineering patterns for production deployment, and how to build similar solutions with AWS services.
AI bots ignore evidence. Can we trust them with science?
Scientists rethink their ideas after experiments. AI agents struggle to learn from evidence and recognize when an idea is obviously incorrect.
Coding agents in the social sciences
Results from a survey of 1,260 social scientists about AI and coding agent use.
Google I/O 2026: tech giant launches autonomous AI era, transforming search at cost to core business
Google unveils Gemini agents and a generative search overhaul in a direct push against OpenAI and Perplexity, risking ad revenue, publisher ties, privacy and margins as it tries to preserve its internet dominance
DuckDuckGo installs are up 30% as users reject being ‘force-fed’ Google’s AI Search
Google overhauled Search at I/O 2026, replacing blue links with AI agents. The backlash has been swift. DuckDuckGo app installs spiked 30% as users seek a way out.
Millions of AI agents imperiled by critical vulnerability in open source package
BadHost" was found in Starlette, a package with 325 million weekly downloads.
AI Agents Plunged the Tech World Into Chaos. Here’s Exactly How That Happened
The definitive story of how Claude Code and OpenClaw kicked off computing’s biggest transformation possibly ever.
Philadelphia Eagles undrafted free agents to watch at OTAs
With the June 9-10 mandatory mini-camp just two weeks away, we're looking at five undrafted free agents to watch.
Fujitsu develops self-evolving multi-AI agent technology that learns and adapts to business operations
Fujitsu Limited today announced the development of a self-evolving multi-AI agent technology that enables multiple AI agents to perform tasks as a team, continuously and safely learning from daily execution results, human feedback, policy revisions, and specification changes.
Linux 7.1-rc5 Released With Fixes Ramping Up From AI Coding Agents
In the road to releasing Linux 7.1 in June, out today is Linux 7.1-rc5 that continues coming on heavy with fixes.
What happened to the Trump administration’s in-your-face mass deportations?
The White House has intentionally retreated from its in-your-face deportation approach after confrontations between federal and state officials in multiple states erupted in Minneapolis earlier this year, when video of masked agents killing protesters sparked outrage and protest.
OpenClaw passed 300,000 GitHub stars. Then Google launched Spark.
Google's Gemini Spark launches as a 24/7 cloud agent, splitting AI agents between managed convenience and OpenClaw's self-hosted control.
Replacing RAG with bash cut AI retrieval costs 30%
DCI lets AI agents search raw files with grep and bash instead of embeddings — boosting accuracy 11 points and cutting retrieval costs 30% on complex tasks.
A framework for longitudinal health AI agents
Nature Health - This Perspective outlines the main components needed to design AI models that can assist individuals in the long-term management of their health, formalizing also the shortcomings...
Google is pitching an AI agent ecosystem to consumers who may not buy it
The AI agents are coming. A lot of them.
Trump Gets His Payback, but It Comes at a Cost in Congress
Republican senators are angry the president is working to unseat their colleagues. But he is also creating more free agents in his own party in Congress willing to defy him.
Google expands AI push at I/O with enterprise-focused Gemini upgrades and smarter search tools
Alphabet Inc. ’s Google (NASDAQ:GOOG) on Tuesday introduced a range of new artificial intelligence tools at its annual I/O developer conference, including AI agents integrated directly into Search and a faster, lower-cost Gemini model aimed at enterprise customers.
Three Eagles free agents remain unsigned ahead of OTAs
The Philadelphia Eagles continue preparing for OTAs while several former veterans, including Adoree' Jackson and Ogbo Okoronkwo, remain unsigned in free agency.
Google announces glasses are back and search is getting an AI makeover
At annual I/O conference, company debuts a product for everyday consumers to create autonomous AI agents
Google to release smart glasses and add AI ‘agents’ to search engine
CEO Sundar Pichai says features powered by new Gemini model will close gap with Anthropic and OpenAI
Everything Announced at Google I/O 2026: Gemini, Search, Smart Glasses
Google is sprucing up its Gemini models, revamping search, and enabling AI agents in everything. There are also some spiffy new smart glasses coming this fall.
Google Antigravity 2.0 goes after Claude Code and OpenAI Codex with a full agent-first rebuild
Google has taken the IDE off Antigravity. What is left, announced at I/O 2026, is a standalone desktop app built around agents rather than a code editor with AI bolted on.
Google flips Antigravity into an agentic dev suite, AI Studio app lands on Android
Google's Antigravity 2.0 is here to build and manage agents to let them loose on your latest projects, as AI Studio Android app arrives.
With Gemini 3.5 Flash, Google bets its next AI wave on agents, not chatbots
Google launched Gemini 3.5 Flash, its most powerful coding and agentic AI model yet, at the company's annual developer conference. It is capable of autonomously executing complex tasks and building software from scratch.
How to use Google’s new information agents
Google is launching AI-powered “information agents” that can monitor topics in the background and proactively alert users to updates and changes.
How to use Google’s new AI agents to go beyond your standard searches
Google is launching AI-powered “information agents” that can monitor topics in the background and proactively alert users to updates and changes.
Google gets ‘intelligent’ Search box redesign, information agents, mini apps, & more
Google Search received big updates at I/O 2026. For starters, AI Mode is now powered by Gemini 3.5 Flash globally...
Introducing new Surface devices built for business and AI acceleration
Every IT leader and partner we work with is navigating the same challenges: AI is changing the demands of how teams work, security threats are growing more sophisticated and agents are changing investment decisions for the future. All under greater s
Meet Dr. Claw: The open-source AI assistant revolutionizing the research workflow
Lehigh University researchers have built the first “AI for Science” software tool designed to support the entire project workflow for research scientists. Dr. Claw is an open-source, full-stack AI research assistant that helps users refine ideas, conduct literature reviews, run experiments, draft and review papers, write grant proposals, and build presentations. It eliminates the need to toggle between specialized AI tools—such as deep research agents for source evaluation or code generators for data analysis—by unifying these capabilities within a single interface.